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Future Internet
Special Issues
Self-Driving Networks (SelfDN) and Artificial Intelligence
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Self-Driving Networks (SelfDN) and Artificial Intelligence

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: closed (30 October 2022) | Viewed by 2983

Special Issue Editor

Dr. Ali Tizghadam

E-Mail Website
Guest Editor
Technology Strategy and Business Transformation, TELUS Communications, Toronto, ON K1P 0A6, Canada
Interests: AI in telecom and networking; software-defined networking; intent-based networking; self-driving networks; network science for communications; cloud computing; intelligent traffic engineering; multi-layer orchestration, smart city; IoT

Special Issue Information

Dear Colleagues,

The networking community has experienced a radical mindset shift in the last 15 years after the emergence of Software-Defined Networking as a blueprint to revolutionize network architecture and practices in the control plane by separating the life cycle of the software from the hardware that the networking software resides in. In parallel, the ICT world has gone through some transformational changes from the inception of the idea of Autonomic Computing, where the objective was to run the compute infrastructure in a self-x manner (self-configuring, self-healing, self-optimizing, etc.). The idea of Autonomic computing then was extended to the networking domain and got more mature and presented itself in the form of Intent-Based Networking (IBN). In both proposals, the foundational idea is the existence of negative feedback control loops to guide the network control system towards the intent or objective at hand while keeping the performance for existing services at the agreed level.

Self-Driving Networks (SelfDN) build on the concept of IBN and tries to achieve the goal of running a network autonomously while keeping all services up and running within given service level objectives. SelfDN is in fact trying to realize the same dream of self-driving cars in transportation networks but in the context of network control.

Towards this end, we need to disseminate intelligence in the mix by employing ideas from the AI world. This Special Issue aims to focus on the most recent advancements in SelfDN with a special interest in machine learning methods to build the intelligent feedback loops required to achieve the dream of self-driving networks.

Dr. Ali Tizghadam
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Software-Defined Networking (SDN)
  • Self-Driving Networks (SelfDN)
  • transportation network
  • machine learning
  • ICT
  • Artificial Intelligent (AI)
  • autonomic computing

Published Papers (1 paper)

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Research

23 pages, 985 KiB  
Article
Multi-Agent-Based Traffic Prediction and Traffic Classification for Autonomic Network Management Systems for Future Networks
by Sisay Tadesse Arzo, Zeinab Akhavan, Mona Esmaeili, Michael Devetsikiotis and Fabrizio Granelli
Future Internet 2022, 14(8), 230; https://doi.org/10.3390/fi14080230 - 28 Jul 2022
Cited by 4 | Viewed by 2622
Abstract
Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should [...] Read more.
Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best. Full article
(This article belongs to the Special Issue Self-Driving Networks (SelfDN) and Artificial Intelligence)
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